A multiplicative seasonal component in commodity derivative pricing

In this paper, we focus on a seasonal jump-diffusion model to price commodity derivatives. We propose a novel approach to estimate the functions of the risk-neutral processes directly from data in the market, even when a closed-form solution for the model is not known. Then, this new approach is applied to price some natural gas derivative contracts traded at New York Mercantile Exchange (NYMEX). Moreover, we use nonparametric estimation techniques in order to avoid arbitrary restrictions on the model. After applying this approach, we find that a jump-diffusion model allowing for seasonality outperforms a standard jump-diffusion model to price natural gas futures. Furthermore, we also show that there are considerable differences in the option prices and the risk premium when we consider seasonality or not. These results have important implications for practitioners in the market.